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Bayesian Optimization of photonic curing process for flexible perovskite photovoltaic devices
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DOI：10.1016/j.solmat.2022.112055.png)
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Abstract
En 中文
Photonic curing is a thin-film processing technique that can enable high-throughput perovskite solar cell (PSC) manufacturing. However, photonic curing has many variables that can affect the processing outcome, making optimization challenging. Here, we introduce Bayesian Optimization (BO), a machine-learning framework, to optimize the power conversion efficiency (PCE) of photonically cured MAPbI3 PSCs on ITO-coated Willow Glass. We apply BO with four input variables-MAPbI3 concentration, additive CH2I2 volume, pulse voltage, and pulse length. These input variables were dynamically adjusted in response to the new data, an example of a human -machine partnership. With the limited experimental budget of 48 conditions, we achieved a champion PCE of 11.42% and predicted 14 new conditions resulting in >10% PCE. Beyond simple optimization, we examined the relationships between pairs of inputs with two-dimensional contour plots and investigated the relative impor-tance of each input to gain insight into photonic curing. We demonstrate that BO is a powerful tool in process optimization and can be adapted to other PSC manufacturing cases.
Keywords:
Perovskite solar cell
Bayesian optimization
Photonic curing
SHarply additive explanation
Machine learning
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6.3
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1.2W
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3.6W
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